Constrained Health Care Resources Research Articles

Multicentre external validation of the Neonatal Healthcare-associated infectiOn Prediction (NeoHoP) score: a retrospective case–control study

Background and objectivesNeonatal mortality due to severe bacterial infections is a pressing global issue, especially in low-middle-income countries (LMICs) with constrained healthcare resources. This study aims to validate the Neonatal...

Self-management needs, strategies and support for individuals with sickle cell disease in developing countries: a scoping review

IntroductionSickle cell disease (SCD) poses a significant global health burden, particularly affecting individuals in developing countries with constrained healthcare resources. While research on self-management in the context of SCD is...

Effects of the ActiveHip+ mHealth intervention on the recovery of older adults with hip fracture and their family caregivers: a multicentre open-label randomised controlled trial

Mobile health (mHealth) systems are a promising alternative for rehabilitation of hip fracture, addressing constrained healthcare resources. Half of older adults fails to recover their pre-fracture routines, which imposes a burden on caregivers. We aimed to test the effectiveness of the 3-month ActiveHip+mHealth intervention on physical and psychological outcomes of older adults with hip fracture and their family caregivers. In a multicentre open-label randomised controlled trial conducted across 3 hospitals in Andalusia (Spain), patients older than 65 with a hip fracture, who were previously independent and lacked cognitive impairment were recruited alongside with their caregivers. Participants were randomly allocated (1:1) to the intervention group (ActiveHip+) or control (usual care) group. The intervention group underwent a 12-week health education and tele-rehabilitation programme through the ActiveHip+mHealth intervention. The primary outcome, physical performance, was assessed using the Short Physical Performance Battery at three time points: at hospital discharge (baseline), 3-month after surgery (post intervention) and 1-year after surgery follow-up. Primary analyses of primary outcomes and safety data followed an intention-to-treat approach. This study is registered at ClinicalTrials.gov, NCT04859309. Between June 1st, 2021 and June 30th, 2022 data from 105 patients and their caregivers were analysed. Patients engaged in the ActiveHip+mHealth intervention (mean 7.11 points, SE 0.33) showed higher physical performance compared with patients allocated in the control group (mean 5.71 points, SE 0.32) at 3 months after surgery (mean difference in change from baseline 1.40 points, SE 0.36; puncorrected=0.00011). These benefits were not maintained at 1-year after surgery follow-up (mean difference in change from baseline 0.19 points, SE 0.47; puncorrected=0.68). No adverse events, including falls and refractures, were reported during the tele-rehabilitation sessions. At 3-months, the intervention group had 2 falls, compared to 4 in the control group, with no observed refractures. At the 1-year follow-up, the intervention group experienced 7 falls and 1 refracture, while the control group had 13 falls and 2 refractures. This study suggests that the ActiveHip+mHealth intervention may be effective for recovering physical performance in older adults with hip fracture. Importantly, the implementation of ActiveHip+into daily clinical practice may be feasible and has already been adopted in 18 hospitals, mostly in Spain but also in Belgium and Portugal. Thus, ActiveHip+could offer a promising solution when rehabilitation resources are limited. However, its dependence on caregiver support and the exclusion of participants with cognitive impairment makes it necessary to be cautious about its applicability. In addition, the non-maintenance of the effectiveness at 1-year follow-up highlights the need of refinement the ActiveHip+intervention to promote long-lasting behavioural changes. EIT Health and the Ramón y Cajal 2021 Excellence Research Grant action from the Spanish Ministry of Science and Innovation.

Self-management needs, strategies and support for sickle cell disease in developing countries: a scoping review protocol

IntroductionSickle cell disease (SCD) poses a significant global health burden, particularly affecting individuals in developing countries with constrained healthcare resources. While research on self-management in the context of SCD is...

Nurses Supporting Harm Reduction: How Take-Home Naloxone is Conceived in the Context of Neoliberalism

Introduction – Individuals must be personally invested in their own recovery journey; however, the neoliberal perspective absolves the state of responsibility of this work and makes promotion of health merely an individual action. Naloxone distribution, as a harm reduction strategy, is presented herein as one practice engaged by nurses that demonstrates philosophical tension between neoliberalism and harm reduction.
 Background Literature – The research literature supporting the provision of take-home naloxone (THN), non-medically administered, is significant and broad.
 Discussion – The problem with neoliberal discourses of constrained healthcare resources in this case is that without broad availability of naloxone, drug poisonings will continue unchecked. There is an ethical call to nurses to support broad distribution of naloxone regardless of the costs involved.
 Conclusion – THN is not only a best practice to reduce the harms of substance use, but it is also a political and philosophical act to hand over the control of public health resources to the public.

Navigating the Legal and Ethical Terrain of Artificial Intelligence in Enhancing Patient Safety in Nigeria

The emergence of Artificial Intelligence (AI) significantly impacts the understanding of medical errors, minimizes their occurrence, and provides contextual solutions for patient safety in Nigeria.Given the country's expanding population and constrained healthcare resources, the potential significance of AI in enhancing patient safety in Nigeria cannot be overstated. There is a rapid trend to integrate AI into Nigeria's healthcare system, however, this raises concerns about algorithm bias and privacy. This study explores the ethical and legal implications of deploying AI for patient safety in Nigeria and assesses how the existing Nigerian legal frameworks address these concerns. This research shows that although bias and discrimination are generally prohibited by the Constitution of the Federal Republic of Nigeria and other legal instruments, algorithmic bias which arises from the use of AI is not catered for by these laws. In addition, despite current privacy and confidentiality safeguards, AI in healthcare presents unique and novel challenges that the legislation does not yet address. Therefore, developing a new legal and governance structure to address both present and prospective challenges of AI in the health sector is extremely useful.

The Statewide Patient Load Balancing Work of Washington State's Medical Operations Coordination Center.

Load balancing of constrained healthcare resources has become a critical aspect of assuring access to care during periods of pandemic related surge. These impacts include patient surges, staffing shortages, and limited access to specialty resources. This research focuses on the creation and work of a novel statewide coordination center, the Washington Medical Coordination Center (WMCC), whose primary goal is the load balancing of patients across the healthcare continuum of Washington State. This article discusses the origins, development, and operations of the WMCC including key partners, cooperative agreements, and structure necessary to create a patient load balancing system on a statewide level. As of April 21, 2022, the WMCC received 3821 requests from Washington State hospitals. Nearly 90% were received during the pandemic surge. Nearly 75% originated from rural hospitals that are most often limited in their ability to transfer patients when referral centers are also overwhelmed. The WMCC served as an effective tool to carry out patient load balancing activities during the COVID-19 pandemic surge in Washington State. It (the WMCC) has been shown to be an equity enhancing, cost effective means of managing healthcare surge events across a broad geographic region.

Process mining for healthcare decision analytics with micro-costing estimations

Managing constrained healthcare resources is an important and inescapable role of healthcare decision makers. Allocative decisions are based on downstream consequences of changes to care processes: judging whether the costs involved are offset by the magnitude of the consequences, and therefore whether the change represents value for money. Process mining techniques can inform such decisions by quantitatively discovering, comparing and detailing care processes using recorded data, however the scope of techniques typically excludes anything ‘after-the-process’ i.e., their accumulated costs and resulting consequences. Cost considerations are increasingly incorporated into process mining techniques, but the majority of healthcare costs for service and overhead components are commonly apportioned and recorded at the patient (trace) level, hiding event level detail. Within decision-analysis, event-driven and individual-level simulation models are sometimes used to forecast the expected downstream consequences of process changes, but are expensive to manually operationalise. In this paper, we address both of these gaps within and between process mining and decision analytics, by better linking them together. In particular, we introduce a new type of process model containing trace data that can be used in individual-level or cohort-level decision-analytical model building. Furthermore, we enhance these models with process-based micro-costing estimations. The approach was evaluated with health economics and decision modelling experts, with discussion centred on how the outputs could be used, and how similar information would otherwise be compiled.

Financial Burden and Average Cost of COVID-19 per Patient Admitted to the Intensive Care Unit in Kuwait

Introduction: COVID-19 pandemic imposed a massive impact on constrained healthcare resources especially Intensive Care Units beds. Nevertheless, few studies have explored direct medical costs of ICU admissions and the financial burden associated with them.Aim: Our aim is to estimate the average direct medical cost of COVID-19 admitted to ICU per patient and per diem as well as the associated financial burden.Methods: A stochastic financial model was developed in accordance with Kuwait Task Force guidelines for COVID-19 management in ICU.Results: Our results showed the average cost of COVID-19 patients admitted in the ICU per patient and per diem to be 16,471 KWD (53,354 USD) and 1,643 KWD (5,422 USD) respectively. While the financial burden over one fiscal year amounted to 15,795,830 KWD (52,126,239 USD).Conclusion: This estimate can guide policy makers, researchers and financial analysts to follow a data driven decisions in planning and budgeting healthcare resources for this concurrent event or similar future events.

Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications.

The recent outbreak of novel coronavirus disease (COVID-19) has resulted in healthcare crises across the globe. Moreover, the persistent and prolonged complications of post-COVID-19 or long COVID are also putting extreme pressure on hospital authorities due to the constrained healthcare resources. Out of many long-lasting post-COVID-19 complications, heart disease has been realized as the most common among COVID-19 survivors. The motivation behind this research is the limited availability of the post-COVID-19 dataset. In the current research, data related to post-COVID complications are collected by personally contacting the previously infected COVID-19 patients. The dataset is preprocessed to deal with missing values followed by oversampling to generate numerous instances, and model training. A binary classifier based on a stacking ensemble is modeled with deep neural networks for the prediction of heart diseases, post-COVID-19 infection. The proposed model is validated against other baseline techniques, such as decision trees, random forest, support vector machines, and artificial neural networks. Results show that the proposed technique outperforms other baseline techniques and achieves the highest accuracy of 93.23%. Moreover, the results of specificity (95.74%), precision (95.24%), and recall (92.05%) also prove the utility of the adopted approach in comparison to other techniques for the prediction of heart diseases.

Mitigating the Spread of Measles under Constrained Health Care Resources in Tanzania using Social Contact Network Models

Tanzania revaccinates individuals during measles outbreaks, despite having scant healthcare resources. We construct epidemiological models of measles spread by employing a hybrid of existing social contact networks models to develop SEIR simulation model. Using demographic and measles surveillance data from three rural villages in Tanzania, we simulate the spread of measles and examine which vaccination strategies can effectively control outbreaks. Results strongly indicate the spread of measles largely depends on contact rates among infected individuals within a population. Findings indicate a need for targeted vaccination for children of 6 months to 15 years of age, but equally for unvaccinated older age groups who were born before 1957 or missed the second dose. This work contributes theoretically and methodologically to existing applications of social contact network models for airborne infectious diseases in areas with health system constraints. It sets out implications for the design of effective vaccination programs for control of measles in Tanzania and in other developing countries.
 Keywords: Vaccination strategies, Control strategies, Social contact network models, Airborne infectious diseases, Measles.

The use of imaging in endometrial cancer prior to potential surgery: Are guidelines being followed?

ObjectivesClinical practice guidelines recommend against routine preoperative axial imaging studies (CT/MRI) for endometrial cancer, except for cases of locally advanced disease or aggressive histologies. This study utilized population-based data to evaluate the use of preoperative imaging and factors associated with its use. MethodsA population-based cohort of women diagnosed with endometrial cancer from 2006 to 2016 were identified from the Ontario Cancer Registry in Ontario, Canada. Patients were excluded if they had: hysterectomy prior to the date of diagnosis, non-epithelial histology or a prior cancer diagnosis within 5 years. Preoperative imaging (CT or MRI) rates were calculated over time. Predictive factors for preoperative imaging use were determined using multi-variable regression analysis. Results17,718 cases were eligible for analysis. From 2006 to 2016, the proportion of patients receiving preoperative imaging increased from 22.2% to 39.3%. In a subgroup of patients with low-risk disease (stage 1, endometrioid adenocarcinoma), imaging increased from 16.3% to 29.5%. Multivariate analysis showed an association between preoperative imaging and advanced stage, advanced grade, non-endometrioid morphology, surgery with a gynecologic oncologist, surgery at a teaching hospital and a later year of diagnosis. From 2006 to 2016, the yearly incidence of endometrial cancer increased from 22.3/100,000 to 36.1/100,000, representing a mean annual increase of 3.6% per year. ConclusionsEndometrial cancer incidence and the use of preoperative imaging are increasing. Factors most associated with preoperative imaging are high-risk features. However, preoperative imaging is still being performed in low-risk patients, indicating non-adherence to guidelines, which has implications for constrained healthcare resources.

Evidence-Based Strategies for the Treatment of Peritoneal Malignancies during Health Care Resource Restriction: The COVID-19 Pandemic.

Background: The COVID-19 pandemic has put enormous pressure on hospital resources, and has affected all aspects of patient care. As operative volumes decrease, cancer surgeries must be triaged and prioritized with careful thought and attention to ensure maximal benefit for the maximum number of patients. Peritoneal malignancies present a unique challenge, as surgical management can be resource intensive, but patients have limited non-surgical treatment options. This review summarizes current data on outcomes and resource utilization to help inform decision-making and case prioritization in times of constrained health care resources. Methods: A rapid literature review was performed, examining surgical and non-surgical outcomes data for peritoneal malignancies. Narrative data synthesis was cross-referenced with relevant societal guidelines. Peritoneal malignancy surgeons and medical oncologists reviewed recommendations to establish a national perspective on case triage and mitigating treatment strategies. Results and Conclusions: Triage of peritoneal malignancies during this time of restricted health care resource is nuanced and requires multidisciplinary discussion with consideration of individual patient factors. Prioritization should be given to patients where delay may compromise resectability of disease, and where alternative treatment options are lacking. Mitigating strategies such as systemic chemotherapy and/or surgical deferral may be utilized with close surveillance for disease stability or progression, which may affect surgical urgency. Unique hospital capacity, and ability to manage the complex post-operative course for these patients must also be considered to ensure patient and system needs are aligned.

Methodological rigor of prognostic models for predicting in-hospital paediatric mortality in low- and middle-income countries: a systematic review protocol

Introduction: In low- and middle-income countries (LMICs) where healthcare resources are often limited, making decisions on appropriate treatment choices is critical in ensuring reduction of paediatric deaths as well as instilling proper utilisation of the already constrained healthcare resources. Well-developed and validated prognostic models can aid in early recognition of potential risks thus contributing to the reduction of mortality rates. The aim of the planned systematic review is to identify and appraise the methodological rigor of multivariable prognostic models predicting in-hospital paediatric mortality in LMIC in order to identify statistical and methodological shortcomings deserving special attention and to identify models for external validation. Methods and analysis: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols. A search of articles will be conducted in MEDLINE, Google Scholar, and CINAHL (via EbscoHost) from inception to 2019 without any language restriction. We will also perform a search in Web of Science to identify additional reports that cite the identified studies. Data will be extracted from relevant articles in accordance with the Cochrane Prognosis Methods’ guidance; the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies. Methodological quality assessment will be performed based on prespecified domains of the Prediction study Risk of Bias Assessment Tool. Ethics and dissemination: Ethical permission will not be required as this study will use published data. Findings from this review will be shared through publication in peer-reviewed scientific journals and, presented at conferences. It is our hope that this study will contribute to the development of robust multivariable prognostic models predicting in-hospital paediatric mortality in low- and middle-income countries. Registration: PROSPERO ID CRD42018088599; registered on 13 February 2018.

Methodological rigor of prognostic models for predicting in-hospital paediatric mortality in low- and middle-income countries: a systematic review protocol.

Introduction: In low- and middle-income countries (LMICs) where healthcare resources are often limited, making decisions on appropriate treatment choices is critical in ensuring reduction of paediatric deaths as well as instilling proper utilisation of the already constrained healthcare resources. Well-developed and validated prognostic models can aid in early recognition of potential risks thus contributing to the reduction of mortality rates. The aim of the planned systematic review is to identify and appraise the methodological rigor of multivariable prognostic models predicting in-hospital paediatric mortality in LMIC in order to identify statistical and methodological shortcomings deserving special attention and to identify models for external validation. Methods and analysis: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols. A search of articles will be conducted in MEDLINE, Google Scholar, and CINAHL (via EbscoHost) from inception to 2019 without any language restriction. We will also perform a search in Web of Science to identify additional reports that cite the identified studies. Data will be extracted from relevant articles in accordance with the Cochrane Prognosis Methods' guidance; the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies. Methodological quality assessment will be performed based on prespecified domains of the Prediction study Risk of Bias Assessment Tool. Ethics and dissemination: Ethical permission will not be required as this study will use published data. Findings from this review will be shared through publication in peer-reviewed scientific journals and, presented at conferences. It is our hope that this study will contribute to the development of robust multivariable prognostic models predicting in-hospital paediatric mortality in low- and middle-income countries. Registration: PROSPERO ID CRD42018088599; registered on 13 February 2018.

Policy-based access control for constrained healthcare resources in the context of the Internet of Things

The Internet of Things (IoT), smart sensors and mobile wearable devices are helping to provide healthcare systems that are more ubiquitous, smarter, faster and easily accessible to users. However, security is a significant concern for the IoT, with access control being one of the major issues. With the growing size and presence of these systems an important question is how to manage policies in a manner that is both scalable and flexible. In this paper, we propose an access control architecture for constrained healthcare resources in the IoT. Our policy-based approach provides fine-grained access for authorized users to services while protecting valuable resources from unauthorized access. We use a hybrid approach by employing attributes, roles and capabilities for our authorization design. We apply attributes for role membership assignment and in permission evaluation. Membership of roles grants capabilities. The capabilities which are issued may be parameterized based on further attributes of the user and are then used to access specific services provided by IoT devices. This significantly reduces the number of policies required for specifying access control settings. The proposed scheme is XACML driven. We have implemented a proof of concept prototype and provide a detailed performance analysis of the implementation. Evaluation results show that, our approach requires minimal additional overhead when compared to other proposals employing capabilities for access control in the IoT.

Internet of Things (IoT) enabled assistive care services: Designing for value and trust

The rising elderly demographic, often with long-term conditions, represents a significant challenge globally in terms of planning for the efficient use of increasingly expensive and constrained health care resources. The internet of things (IoT) emerged as a disruptive and transformative new technology that could potentially stimulate development of new innovative assisted living health and care services. In this paper, we argue that as the human agency and relationship intrinsically associated with care get transferred to the material agency of smart technology, value and trust should be a vital consideration for designing such services. Drawing on interdisciplinary perspectives from the literature on services innovation, design science and trust in relation to healthcare technologies, we present a conceptual framework that articulates various levels of trust among the concerned stakeholders in the service ecosystem and suggests value-sensitive design considerations, anchored on the principles of trust, for future IoT-enabled assistive care services.

Diagnostic performance of random urine samples using albumin concentration vs ratio of albumin to creatinine for microalbuminuria screening in patients with diabetes mellitus: a systematic review and meta-analysis.

A random urine sample measuring the albumin concentration (UAC) without simultaneously measuring the urinary creatinine is less expensive than measuring the ratio of albumin to creatinine (ACR), but comparisons of their diagnostic performance for microalbuminuria screening among patients with diabetes mellitus (DM) have not been undertaken in previous meta-analyses. To compare the diagnostic performance of the UAC vs the ACR in random urine samples for microalbuminuria screening among patients with DM. Electronic literature searches of PubMed, MEDLINE, and Scopus for English-language publications from the earliest available date of indexing through July 31, 2012. Clinical studies assessing the UAC or the ACR of random urine samples in detecting the presence of microalbuminuria among patients with DM using a urinary albumin excretion rate of 30 to 300 mg/d in 24-hour timed urine collections as the criterion standard. Bivariate random-effects models for analysis and pooling of the diagnostic performance measures across studies, as well as comparisons between different screening tests. The primary end point was the diagnostic performance measures of the UAC or the ACR in random urine samples, as well as comparisons between them. We identified 14 studies, with a total of 2078 patients; 9 studies reported on the UAC, and 12 studies reported on the ACR. Meta-analysis showed pooled sensitivities of 0.85 and 0.87 for the UAC and the ACR, respectively, and pooled specificities of 0.88 and 0.88, respectively. No differences in sensitivity (P = .70), specificity (P = .63), or diagnostic odds ratios (P = .59) between the UAC and the ACR were found. The time point of urine collection did not affect the diagnostic performance of either test. The UAC and the ACR yielded high sensitivity and specificity for the detection of microalbuminuria. Because the diagnostic performance of the UAC is comparable to that of the ACR, our findings indicate that the UAC of random urine samples may become the screening tool of choice for the population with DM, considering the rising incidence of DM and the constrained health care resources in many countries.

Most Important Outcomes Research Papers on Device Therapies for Cardiomyopathies

Disorders of the cardiac muscle or cardiomyopathies are a broad, yet collectively common, group of conditions. Despite the heterogeneous etiologies, mode of death from these conditions is remarkably similar - progressive decline in cardiac function leading to intractable heart failure (HF) and sustained ventricular arrhythmias resulting in sudden cardiac death (SCD). Nearly 50% of patients die within 5 years of a HF diagnosis.1 Indeed, in the United States, HF alone is thought to cause 55,000 deaths per year2 and further contribute to 1 in 9 deaths overall.1 However, while advanced HF and the risk of SCD were once thought to be untreatable, technological advances has seen the emergence of device therapies as viable treatment options. Specifically, implantable cardioverter-defibrillator (ICD) therapy for treatment of ventricular arrhythmias, cardiac resynchronization therapy (CRT) for restoring cardiac synchrony and mechanical efficiency, and ventricular assist device (VAD) therapy to temporarily or permanently replace the function of the failing heart, have all emerged as highly efficacious therapies. The expanding use of device therapies, however, poses many challenges. First, while the indications for these devices are well summarized in clinical guidelines,3,4 considerable hurdles remain in ensuring eligible patients receive these therapies.5 By the same token, establishing the safety and effectiveness of these therapies in populations that are found in clinical practice, yet commonly excluded from trials, such as the elderly6 and uncommon forms of cardiomyopathies,7 is a high priority. Second, rapid dissemination of technologies frequently results in disparities in care. Indeed, age, gender, and racial disparities, in both receipt of these devices and outcomes following implantation, have been well documented. Whether these disparities have persisted, and the potential causative mechanisms underlying these disparities, however, are uncertain.8,9 Third, these devices are not without significant untoward effects; understanding …

Identifying the medical practice after total hip arthroplasty using an integrated hybrid approach

A critical option of total hip arthroplasty (THA) is considered only when tried more conservative treatments but continued to have pain, stiffness, or problems with the function of ones hip. THA plays one of major concerns under the waves of the rapid growth of aging populations and the constrained health care resources in Taiwan. Moreover, prior studies indicated that imbalanced class distribution problems do exist in the constructed classification model and cause seriously negative effects on model performances in the health care industry. Therefore, this study proposes an integrated hybrid approach to provide an alternate method for classifying the quality (e.g., the staying length in hospital) of medical practice with an imbalanced class problem after performing a THA procedure for hip replacement patients and their doctors in the health care industry. The proposed approach is constituted by seven components: expert knowledge, global discretization, imbalanced bootstrap technique, reduct and core methods, rough sets, rule induction, and rule filter. The proposed approach is illustrated in practice by examining an experimental dataset from the National Health Insurance Research Database (NHIRD) in Taiwan. The experimental results reveal that the proposed approach has better performance than the listed methods under evaluation criteria. The output created by the rough set LEM2 algorithm is a comprehensible decision rule set that can be applied in knowledge-based health care services as desired. The analytical results provide useful THA information for both academics and practitioners and these results could be applicable to other diseases or to other countries with similar social and cultural practices.